




"""simple docstring"""




import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


lowerCAmelCase				:     Union[str, Any]					=				{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

lowerCAmelCase				:     Tuple					=				{
    """169M""": 768,
    """430M""": 1024,
    """1B5""": 2048,
    """3B""": 2560,
    """7B""": 4096,
    """14B""": 5120,
}



def        a__					(							snake_case__							)		->   List[Any]:
			lowerCamelCase      =   list(state_dict.keys()							)
			for name in state_dict_keys:
						lowerCamelCase      =   state_dict.pop(snake_case__							)
						# emb -> embedding
						if name.startswith("""emb."""							):
									lowerCamelCase      =   name.replace("""emb."""			,					"""embeddings."""							)
						# ln_0 -> pre_ln (only present at block 0)
						if name.startswith("""blocks.0.ln0"""							):
									lowerCamelCase      =   name.replace("""blocks.0.ln0"""			,					"""blocks.0.pre_ln"""							)
						# att -> attention
						lowerCamelCase      =   re.sub(R"""blocks\.(\d+)\.att"""			,					R"""blocks.\1.attention"""			,					snake_case__							)
						# ffn -> feed_forward
						lowerCamelCase      =   re.sub(R"""blocks\.(\d+)\.ffn"""			,					R"""blocks.\1.feed_forward"""			,					snake_case__							)
						# time_mix_k -> time_mix_key and reshape
						if name.endswith(""".time_mix_k"""							):
									lowerCamelCase      =   name.replace(""".time_mix_k"""			,					""".time_mix_key"""							)
						# time_mix_v -> time_mix_value and reshape
						if name.endswith(""".time_mix_v"""							):
									lowerCamelCase      =   name.replace(""".time_mix_v"""			,					""".time_mix_value"""							)
						# time_mix_r -> time_mix_key and reshape
						if name.endswith(""".time_mix_r"""							):
									lowerCamelCase      =   name.replace(""".time_mix_r"""			,					""".time_mix_receptance"""							)

						if name != "head.weight":
									lowerCamelCase      =   """rwkv.""" + name

						lowerCamelCase      =   weight
			return state_dict



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__=None			,					snake_case__=None			,					snake_case__=False			,					snake_case__=None							)		->   str:
			# 1. If possible, build the tokenizer.
			if tokenizer_file is None:
						print("""No `--tokenizer_file` provided, we will use the default tokenizer."""							)
						lowerCamelCase      =   5_02_77
						lowerCamelCase      =   AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""							)
			else:
						lowerCamelCase      =   PreTrainedTokenizerFast(tokenizer_file=snake_case__							)
						lowerCamelCase      =   len(snake_case__							)
			tokenizer.save_pretrained(snake_case__							)

			# 2. Build the config
			lowerCamelCase      =   list(NUM_HIDDEN_LAYERS_MAPPING.keys()							)
			if size is None:
						# Try to infer size from the checkpoint name
						for candidate in possible_sizes:
									if candidate in checkpoint_file:
												lowerCamelCase      =   candidate
												break
						if size is None:
									raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""							)
			if size not in possible_sizes:
						raise ValueError(F'`size` should be one of {possible_sizes}, got {size}.'							)

			lowerCamelCase      =   RwkvConfig(
			    vocab_size=snake_case__			,					num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,					hidden_size=HIDEN_SIZE_MAPPING[size]			,					)
			config.save_pretrained(snake_case__							)

			# 3. Download model file then convert state_dict
			lowerCamelCase      =   hf_hub_download(snake_case__			,					snake_case__							)
			lowerCamelCase      =   torch.load(snake_case__			,					map_location="""cpu"""							)
			lowerCamelCase      =   convert_state_dict(snake_case__							)

			# 4. Split in shards and save
			lowerCamelCase ,  lowerCamelCase      =   shard_checkpoint(snake_case__							)
			for shard_file, shard in shards.items():
						torch.save(snake_case__			,					os.path.join(snake_case__			,					snake_case__							)							)

			if index is not None:
						lowerCamelCase      =   os.path.join(snake_case__			,					snake_case__							)
						# Save the index as well
						with open(snake_case__			,					"""w"""			,					encoding="""utf-8"""							) as f:
									lowerCamelCase      =   json.dumps(snake_case__			,					indent=2			,					sort_keys=snake_case__							) + """\n"""
									f.write(snake_case__							)

						# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
						print(
						    """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""							)
						lowerCamelCase      =   list(shards.keys()							)

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									lowerCamelCase      =   torch.load(os.path.join(snake_case__			,					snake_case__							)							)
									torch.save({k: v.cpu().clone() for k, v in state_dict.items()}			,					os.path.join(snake_case__			,					snake_case__							)							)

			del state_dict
			gc.collect()

			if push_to_hub:
						if model_name is None:
									raise ValueError("""Please provide a `model_name` to push the model to the Hub."""							)
						lowerCamelCase      =   AutoModelForCausalLM.from_pretrained(snake_case__							)
						model.push_to_hub(snake_case__			,					max_shard_size="""2GB"""							)
						tokenizer.push_to_hub(snake_case__							)


if __name__ == "__main__":
					lowerCAmelCase				:     Optional[int]					=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
					)
					parser.add_argument(
					    """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
					)
					parser.add_argument(
					    """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
					)
					parser.add_argument(
					    """--tokenizer_file""",
					    default=None,
					    type=str,
					    help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
					)
					parser.add_argument(
					    """--size""",
					    default=None,
					    type=str,
					    help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					    help="""Push to the Hub the converted model.""",
					)
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help="""Name of the pushed model on the Hub, including the username / organization.""",
					)

					lowerCAmelCase				:     List[Any]					=				parser.parse_args()
					convert_rmkv_checkpoint_to_hf_format(
					    args.repo_id,
					    args.checkpoint_file,
					    args.output_dir,
					    size=args.size,
					    tokenizer_file=args.tokenizer_file,
					    push_to_hub=args.push_to_hub,
					    model_name=args.model_name,
					)









"""simple docstring"""




def        a__					(							)		->   int:
			return 1



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else two_pence(x - 2							) + one_pence()



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else five_pence(x - 5							) + two_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else ten_pence(x - 10							) + five_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else twenty_pence(x - 20							) + ten_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else fifty_pence(x - 50							) + twenty_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else one_pound(x - 1_00							) + fifty_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else two_pound(x - 2_00							) + one_pound(snake_case__							)



def        a__					(							snake_case__ = 2_00							)		->   int:
			return two_pound(snake_case__							)


if __name__ == "__main__":
					print(solution(int(input().strip())))



